Data Scientist II

At Early Warning, we’ve powered and protected the U.S. financial system for over thirty years with cutting‑edge solutions like Zelle®, Paze℠, and so much more. As a trusted name in payments, we partner with thousands of institutions to increase access to financial services and protect transactions for hundreds of millions of consumers and small businesses.

Positions located in Scottsdale, San Francisco, Chicago, or New York follow a hybrid work model to allow for a more collaborative working environment.

Candidates responding to this posting must independently possess the eligibility to work in the United States, for any employer, at the date of hire. This position is ineligible for employment Visa sponsorship.

This position serves as a data science team member in the company delivering leading edge machine learning and artificial intelligence concepts from start to finish in collaboration with senior technical and business leaders. This includes understanding the business problem/need, exploring, and aggregating data, building and validating algorithms, and deploying completed models to deliver business results.

• Explore and aggregate data independently to uncover data anomalies that impact algorithm performance
• End to end feature engineering – brainstorm, create, validate, down-select, etc.
• Write production level code in a dynamic, start‑up environment
• Solve complex problems using terabyte sized data sets
• Apply a variety of machine learning techniques to a business problem to arrive at the optimal approach
• Partner with Product and Engineering teams to solve problems and identify trends and opportunities
• Explain and visualize results and algorithm performance to non‑technical audiences
• Support the company’s commitment to protect the integrity and confidentiality of systems and data

• Bachelor’s Degree in Mathematics, Statistics, Computer Science, Operational Research or related field
• A minimum of 4 years data science, engineering, mathematics, or related work experience is required
• Experience developing data science pipelines & workflows in Python, R or equivalent programming language; experience in writing and tuning SQL; experience handling terabyte sized datasets
• Experience applying various machine learning techniques, and understanding the key parameters that affect their performance
• Experience using ML libraries such as scikit‑learn, mllib, etc.
• Experience using data visualization tools
• Able to write production level code, which is well‑written and explainable
• Interest to do lots and lots of proof of concepts/rapid prototyping
• Ability to effectively communicate findings from complex analyses to non‑technical audiences
• Background and drug screen

• PhD/MSc in Mathematics, Statistics, Computer Science, Operational Research or related field; advanced degree preferred
• Pyspark experience
• Financial or banking experience
• Building fraud and/or risk models
• Knowledge of advanced ML algorithms
• 2+ years of industry experience in machine learning
• Demonstrable track record of dealing well with ambiguity, prioritizing needs, and delivering results in a dynamic environment
• Experience exploring data and finding hidden patterns

The base pay scale for this position in:
Phoenix, AZ / Chicago, IL in USD per year is: $118,000 - $152,000.
San Francisco, CA in USD per year is: $142,000 - $183,000.

Additionally, candidates are eligible for a discretionary incentive plan and benefits.

This pay scale is subject to change and is not necessarily reflective of actual compensation that may be earned, nor a promise of any specific pay for any specific candidate, which is always dependent on legitimate factors considered at the time of job offer. Early Warning Services takes into consideration a variety of factors when determining a competitive salary offer, including, but not limited to, the job scope, market rates and geographic location of a position, candidate’s education, experience, training, and specialized skills or certification(s) in relation to the job requirements and compared with internal equity (peers).

The business actively supports and reviews wage equity…